CAREER: Robots that Help People

职业：帮助人们的机器人

• 批准号: 1652561
• 负责人: Stefanie Tellex
• 金额: $ 54.94万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652561&HistoricalAwards=false
• 关键词: CAREER; Robots; Help; People

As robots become more prevalent, it is crucial to develop ways for people to collaborate with them. This proposal aims to create collaborative robots through a combination of communication, perception, and action. Existing approaches are typically tailored to specific applications; yet people want to talk to robots about everything they can see and do. To address such limitations, this project will create a unified framework to enable robots to communicate with people to learn their needs, plan how to achieve them, and then perceive and act in the world in order to meet those needs. The research will be demonstrated with robots that can assist with household tasks, such as cooking and cleaning, as well as in manufacturing settings, such as collaborative assembly. The project will expose many people to collaborative robotics through an internship program with local high schools, a regional robotics conference, and the Million Object Challenge.This project will create a model, the Human-Robot Collaborative Partially Observable Markov Decision Process, that enables robots to 1) automatically acquire object-oriented models of objects in the physical world; 2) communicate with people to understand their needs and how to meet them; and 3) act to change the world in ways that meet people's needs. Creating a unified framework requires bridging gaps between different aspects of the robotic system. This project focuses on creating a single probabilistic graphical model to represent the robot's states and actions in a hierarchical framework, allowing the robot to make plans that take into account its own uncertainty and to communicate with a person about everything it can see and everything it can do. Focusing on collaboration leads to reformulations of traditional problems in computer vision, planning, and natural language understanding enabling the robot to collaborate in new and more natural ways.

随着机器人变得越来越普遍，开发人们与他们合作的方式至关重要。 该建议旨在通过沟通，感知和行动的结合来创建协作机器人。 现有方法通常是针对特定应用程序量身定制的；然而，人们想与机器人谈论他们可以看到和做的一切。 为了解决此类限制，该项目将创建一个统一的框架，以使机器人能够与人们交流以了解他们的需求，计划如何实现这些需求，然后在世界上感知和行动以满足这些需求。 这项研究将通过可以帮助完成家庭任务的机器人，例如烹饪和清洁以及制造环境，例如协作集会。 该项目将通过与当地高中的实习计划，区域机器人会议和百万个对象挑战揭露许多人的协作机器人技术。该项目将创建一个模型，即人类机器人协作的部分可观察到的马尔可夫决策过程，这使机器人能够使机器人能够自动获得物理世界中面向对象对象对象对象模型的模型； 2）与人们沟通以了解他们的需求以及如何满足他们； 3）采取行动以满足人们需求的方式改变世界。 创建一个统一的框架需要在机器人系统的不同方面之间弥合差距。 该项目着重于创建一个单一的概率图形模型，以在层次结构框架中代表机器人的状态和动作，从而使机器人能够制定计划，以考虑其自身的不确定性并与一个人交流有关它可以看到的一切以及它可以做的一切。 专注于协作会导致对计算机愿景，计划和自然语言理解中传统问题的重新制定，从而使机器人能够以新的和更自然的方式进行协作。

项目成果

Multi-Resolution POMDP Planning for Multi-Object Search in 3D

• DOI: 10.1109/iros51168.2021.9636737
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
• 作者: Zheng, Kaiyu;Sung, Yoonchang;Tellex, Stefanie
• 通讯作者: Tellex, Stefanie

Planning with State Abstractions for Non-Markovian Task Specifications

非马尔可夫任务规范的状态抽象规划

• 发表时间: 2019
• 期刊: Robotics science and systems
• 作者: Yoonseon Oh, Roma Patel

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

部分观测城市规模环境中对象搜索的空间语言理解

• DOI: 10.1109/ro-man50785.2021.9515426
• 发表时间: 2021
• 期刊: ICRA
• 作者: Zheng, Kaiyu;Bayazit, Deniz;Mathew, Rebecca;Pavlick, Ellie;Tellex, Stefanie
• 通讯作者: Tellex, Stefanie

Multi-Object Search using Object-Oriented POMDPs

• DOI: 10.1109/icra.2019.8793888
• 发表时间: 2019-01-01
• 期刊: 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
• 作者: Wandzel, Arthur;Oh, Yoonseon;Tellex, Stefanie
• 通讯作者: Tellex, Stefanie